问题
scala> val map1 = spark.sql("select map('p1', 's1', 'p2', 's2')")
map1: org.apache.spark.sql.DataFrame = [map(p1, s1, p2, s2): map<string,string>]
scala> map1.show()
+--------------------+
| map(p1, s1, p2, s2)|
+--------------------+
|[p1 -> s1, p2 -> s2]|
+--------------------+
scala> spark.sql("select element_at(map1, 'p1')")
org.apache.spark.sql.AnalysisException: cannot resolve '
map1
' given input columns: []; line 1 pos 18; 'Project [unresolvedalias('element_at('map1, p1), None)]
How can we reuse the dataframe map1 in second sql query?
回答1:
map1
is a dataframe with a single column of type map. This column has the name map(p1, s1, p2, s2)
. The dataframe can be queried for example with selectExpr:
map1.selectExpr("element_at(`map(p1, s1, p2, s2)`, 'p1')").show()
prints
+-----------------------------------+
|element_at(map(p1, s1, p2, s2), p1)|
+-----------------------------------+
| s1|
+-----------------------------------+
Another option is to register the dataframe as temporary view and then use a sql query:
map1.createOrReplaceTempView("map1")
spark.sql("select element_at(`map(p1, s1, p2, s2)`, 'p1') from map1").show()
which prints the same result.
来源:https://stackoverflow.com/questions/64107171/how-to-refer-a-map-column-in-a-spark-sql-query